Spaces:
Sleeping
Sleeping
| #!/usr/bin/env python3 | |
| from __future__ import annotations | |
| import rerun as rr | |
| from datasets import load_dataset | |
| from PIL import Image | |
| from tqdm import tqdm | |
| def log_dataset_to_rerun(dataset) -> None: | |
| # Special time-like columns | |
| TIME_LIKE = {"index", "frame_id", "timestamp"} | |
| # Ignore these columns | |
| IGNORE = {"episode_data_index_from", "episode_data_index_to", "episode_id"} | |
| num_rows = len(dataset) | |
| for row_nr in tqdm(range(num_rows)): | |
| row = dataset[row_nr] | |
| # Handle time-like columns first, since they set a state (time is an index in Rerun): | |
| for column_name in TIME_LIKE: | |
| if column_name in row: | |
| cell = row[column_name] | |
| if isinstance(cell, int): | |
| rr.set_time_sequence(column_name, cell) | |
| elif isinstance(cell, float): | |
| rr.set_time_seconds(column_name, cell) # assume seconds | |
| else: | |
| print(f"Unknown time-like column {column_name} with value {cell}") | |
| # Now log actual data columns | |
| for column_name in dataset.column_names: | |
| if column_name in TIME_LIKE or column_name in IGNORE: | |
| continue | |
| cell = row[column_name] | |
| if isinstance(cell, Image.Image): | |
| rr.log(column_name, rr.Image(cell)) | |
| elif isinstance(cell, list): | |
| rr.log(column_name, rr.BarChart(cell)) | |
| elif isinstance(cell, float) or isinstance(cell, int): | |
| rr.log(column_name, rr.Scalar(cell)) | |
| else: | |
| rr.log(column_name, rr.TextDocument(str(cell))) | |
| def main(): | |
| print("Loading dataset…") | |
| # dataset = load_dataset("lerobot/pusht", split="train") | |
| dataset = load_dataset("lerobot/aloha_sim_transfer_cube_human", split="train") | |
| print("Selecting specific episode…") | |
| ds_subset = dataset.filter(lambda frame: frame["episode_id"] == 3) | |
| print("Starting Rerun…") | |
| rr.init("rerun_example_lerobot", spawn=True) | |
| print("Logging to Rerun…") | |
| log_dataset_to_rerun(ds_subset) | |
| if __name__ == "__main__": | |
| main() | |